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  1. Preface
  2. Installing MDM Big Data Relationship Management
  3. Configuring MDM Big Data Relationship Management
  4. Configuring Security
  5. Setting Up the Environment to Process Streaming Data
  6. Configuring Distributed Search
  7. Packaging and Deploying the RESTful Web Services

Installation and Configuration Guide

Installation and Configuration Guide

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Step 3. Deploy MDM Big Data Relationship Management on Spark

Step 3. Deploy MDM Big Data Relationship Management on Spark

You must deploy MDM Big Data Relationship Management on Spark to link, consolidate, or tokenize the input data. Run the setup_realtime.sh script located in the following directory to deploy MDM Big Data Relationship Management on Spark: /usr/local/mdmbdrm-<Version Number>
Use the following command to run the setup_realtime.sh script: 
setup_realtime.sh

--config=configuration_file_name

--rule=matching_rules_file_name

[--consolidate=consolidation_rules_file_name]

[--instanceName=instance_name]

[--zookeeper=zookeeper_connection_string]

[--skipCreateTopic]

[--sparkMaster=deployment_mode]

[--sparkMicroBatchDuration=batch_processing_time]

[--checkpointDirectory=directory_to_store_checkpoint]						

[--partitions=number_of_partitions]

[--replica=number_of_replicas]

[--outputTopic=output_topic_name]
The following table describes the options and arguments that you can specify to run the setup_realtime.sh script:
Option Argument Description
--config configuration_file_name Absolute path and file name of the configuration file.
Ensure that the configuration file is present in the same directory path in all the Spark nodes.
--rule matching_rules_file_name Absolute path and file name of the matching rules file.
The values in the matching rules file override the values in the configuration file.
Ensure that the matching rules file is present in the same directory path in all the Spark nodes.
--consolidate consolidation_rules_file_name
Optional. Absolute path and file name of the consolidation rules file.
Use the consolidation rules file only when you want to consolidate the linked data and create preferred records for all the clusters.
--instanceName instance_name Optional. Name for the Spark instance that processes the input data.
Default is BDRMRTIngestSpark.
--zookeeper zookeeper_connection_string Optional. Connection string to access the ZooKeeper server.
Use the following format for the connection string:
<Host Name>:<Port>[/<chroot>]
The connection string uses the following parameters:
  • Host Name. Host name of the ZooKeeper server.
  • Port. Port on which the ZooKeeper server listens.
  • chroot. Optional. ZooKeeper root directory that you configure in Kafka. Default is /.
The following example connection string uses the default ZooKeeper root directory: server1.domain.com:2182
The following example connection string uses the user-defined ZooKeeper root directory: server1.domain.com:2182/kafkaroot
If you use an ensemble of ZooKeeper servers, you can specify multiple ZooKeeper servers separated by commas.
--skipCreateTopic Required if the topic that you specify in the configuration file already exists in Kafka. Indicates to skip creating the topic.
By default, the script creates the topic.
--partitions number_of_partitions Optional. Number of partitions for the topic. Use partitions to split the data in the topic across multiple brokers. Default is 1.
Ensure that the number of partitions is equal to the number of node managers in the cluster.
--replica number_of_replicas Optional. Number of replicas that you want to create for the topic. Use replicas for high availability purposes.
Default is 1.
--sparkMaster deployment_mode Indicates whether the Spark runs in the standalone or cluster mode.
Use one of the following values:
  • local[*]. Indicates to run Spark locally with as many worker threads as the number of cores on your machine.
  • local[N]. Indicates to run Spark locally with N worker threads.
  • yarn. Indicates to run Spark in the cluster mode.
Default is local[*].
--sparkMicroBatchDuration batch_processing_time Number of seconds to wait before attempting to poll the next batch of data.
Default is 2 seconds.
--checkpointDirectory directory_to_store_checkpoint Absolute path to a HDFS directory or a shared NFS directory to store the checkpoint-related information. Spark uses the checkpoint-related information when a node recovers from a failure.
For example, the following sample directory path stores the checkpoint-related information in HDFS: hdfs:///user/spark/checkpoint
--outputTopic output_topic_name Optional. Name of the topic in Kafka to which you want to publish the output messages. By default, the output messages are not published.
The script does not create the output topic, so ensure that you create the output topic to publish the output messages to it.
For example, the following command runs the script that deploys MDM Big Data Relationship Management on Spark: 
setup_realtime.sh --config=/usr/local/conf/config_big.xml --rule=/usr/local/conf/matching_rules.xml --consolidate=/usr/local/conf/consolidationfile.xml --instanceName=Prospects --zookeeper=10.28.10.345 --skipCreateTopic --partitions=3 --replica=2 --sparkMaster=yarn --sparkMicroBatchDuration=5 --checkpointDirectory=hdfs:///user/spark/checkpoint --outputTopic=InsuranceOutput
When you run the setup_realtime.sh script after an unexpected shutdown of the node, the script recovers Spark from the failure point and ignores other parameters that you specify in the command. If you do not want to recover Spark from the failure point, delete the check point directory before you run the setup_realtime.sh script.

Updated June 27, 2019

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